| predict.lme {nlme} | R Documentation |
The predictions at level i are obtained by adding together the
population predictions (based only on the fixed effects estimates)
and the estimated contributions of the random effects to the
predictions at grouping levels less or equal to i. The resulting
values estimate the best linear unbiased predictions (BLUPs) at level
i. If group values not included in the original grouping factors
are present in newdata, the corresponding predictions will be
set to NA for levels greater or equal to the level at which the
unknown groups occur.
## S3 method for class 'lme'
predict(object, newdata, level = Q, asList = FALSE,
na.action = na.fail, ...)
object |
an object inheriting from class |
newdata |
an optional data frame to be used for obtaining the predictions. All variables used in the fixed and random effects models, as well as the grouping factors, must be present in the data frame. If missing, the fitted values are returned. |
level |
an optional integer vector giving the level(s) of grouping to be used in obtaining the predictions. Level values increase from outermost to innermost grouping, with level zero corresponding to the population predictions. Defaults to the highest or innermost level of grouping. |
asList |
an optional logical value. If |
na.action |
a function that indicates what should happen when
|
... |
some methods for this generic require additional arguments. None are used in this method. |
if a single level of grouping is specified in level, the
returned value is either a list with the predictions split by groups
(asList = TRUE) or a vector with the predictions
(asList = FALSE); else, when multiple grouping levels are
specified in level, the returned object is a data frame with
columns given by the predictions at different levels and the grouping
factors.
José Pinheiro and Douglas Bates bates@stat.wisc.edu
fm1 <- lme(distance ~ age, Orthodont, random = ~ age | Subject)
newOrth <- data.frame(Sex = c("Male","Male","Female","Female","Male","Male"),
age = c(15, 20, 10, 12, 2, 4),
Subject = c("M01","M01","F30","F30","M04","M04"))
## The 'Orthodont' data has *no* 'F30', so predict NA at level 1 :
predict(fm1, newOrth, level = 0:1)